869 research outputs found
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
With the increasing research interest in dialogue response generation, there
is an emerging branch formulating this task as selecting next sentences, where
given the partial dialogue contexts, the goal is to determine the most probable
next sentence. Following the recent success of the Transformer model, this
paper proposes (1) a new variant of attention mechanism based on multi-head
attention, called highway attention, and (2) a recurrent model based on
transformer and the proposed highway attention, so-called Highway Recurrent
Transformer. Experiments on the response selection task in the seventh Dialog
System Technology Challenge (DSTC7) show the capability of the proposed model
of modeling both utterance-level and dialogue-level information; the
effectiveness of each module is further analyzed as well
Development of HIF-1α/HIF-1β heterodimerization inhibitors using a novel bioluminescence reporter assay system for in vitro high throughput screening and in vivo imaging
Tumor growth often outpaces its vascularization, leading to development of a hypoxic tumor microenvironment. In response, an intracellular hypoxia survival pathway is initiated by heterodimerization of hypoxia-inducible factor (HIF)-1α and HIF-1β, which subsequently upregulates the expression of several hypoxia-inducible genes, promotes cell survival and stimulates angiogenesis in the oxygen-deprived environment. Hypoxic tumor regions are often associated with resistance to various classes of radio- or chemotherapeutic agents. Therefore, development of HIF-1α/β heterodimerization inhibitors may provide a novel approach to anti-cancer therapy. To this end, a novel approach for imaging HIF-1α/β heterodimerization in vitro and in vivo was developed in this study. Using this screening platform, we identified a promising lead candidate and further chemically derivatized the lead candidate to assess the structure-activity relationship (SAR). The most effective first generation drug inhibitors were selected and their pharmacodynamics and anti-tumor efficacy in vivo were verified by bioluminescence imaging (BLI) of HIF-1α/β heterodimerization in the xenograft tumor model. Furthermore, the first generation drug inhibitors, M-TMCP and D-TMCP, demonstrated efficacy as monotherapies, resulting in tumor growth inhibition via disruption of HIF-1 signaling-mediated tumor stromal neoangiogenesis
An Empirical Study of Content Understanding in Conversational Question Answering
With a lot of work about context-free question answering systems, there is an
emerging trend of conversational question answering models in the natural
language processing field. Thanks to the recently collected datasets, including
QuAC and CoQA, there has been more work on conversational question answering,
and recent work has achieved competitive performance on both datasets. However,
to best of our knowledge, two important questions for conversational
comprehension research have not been well studied: 1) How well can the
benchmark dataset reflect models' content understanding? 2) Do the models well
utilize the conversation content when answering questions? To investigate these
questions, we design different training settings, testing settings, as well as
an attack to verify the models' capability of content understanding on QuAC and
CoQA. The experimental results indicate some potential hazards in the benchmark
datasets, QuAC and CoQA, for conversational comprehension research. Our
analysis also sheds light on both what models may learn and how datasets may
bias the models. With deep investigation of the task, it is believed that this
work can benefit the future progress of conversation comprehension. The source
code is available at https://github.com/MiuLab/CQA-Study.Comment: Published at AAAI 202
Shilling Black-box Review-based Recommender Systems through Fake Review Generation
Review-Based Recommender Systems (RBRS) have attracted increasing research
interest due to their ability to alleviate well-known cold-start problems. RBRS
utilizes reviews to construct the user and items representations. However, in
this paper, we argue that such a reliance on reviews may instead expose systems
to the risk of being shilled. To explore this possibility, in this paper, we
propose the first generation-based model for shilling attacks against RBRSs.
Specifically, we learn a fake review generator through reinforcement learning,
which maliciously promotes items by forcing prediction shifts after adding
generated reviews to the system. By introducing the auxiliary rewards to
increase text fluency and diversity with the aid of pre-trained language models
and aspect predictors, the generated reviews can be effective for shilling with
high fidelity. Experimental results demonstrate that the proposed framework can
successfully attack three different kinds of RBRSs on the Amazon corpus with
three domains and Yelp corpus. Furthermore, human studies also show that the
generated reviews are fluent and informative. Finally, equipped with Attack
Review Generators (ARGs), RBRSs with adversarial training are much more robust
to malicious reviews
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Person re-identification (re-ID) solves the task of matching images across
cameras and is among the research topics in vision community. Since query
images in real-world scenarios might suffer from resolution loss, how to solve
the resolution mismatch problem during person re-ID becomes a practical
problem. Instead of applying separate image super-resolution models, we propose
a novel network architecture of Resolution Adaptation and re-Identification
Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy
of adversarial learning, we aim at extracting resolution-invariant
representations for re-ID, while the proposed model is learned in an end-to-end
training fashion. Our experiments confirm that the use of our model can
recognize low-resolution query images, even if the resolution is not seen
during training. Moreover, the extension of our model for semi-supervised re-ID
further confirms the scalability of our proposed method for real-world
scenarios and applications.Comment: Accepted to AAAI 2019 (Oral
Well-differentiated gall bladder hepatoid carcinoma producing alpha-fetoprotein: a case report
<p>Abstract</p> <p>Introduction</p> <p>Gall bladder carcinoma is rare, and metastatic gall bladder carcinoma from hepatocellular carcinoma has been reported in only a few patients.</p> <p>Case presentation</p> <p>We present a 73-year-old man with a history of hepatitis B virus-related liver cirrhosis and hepatocellular carcinoma. He received transcatheter arterial chemoembolization, and was diagnosed to have an alpha-fetoprotein producing gall bladder tumor with intraluminal growth. Open cholecystectomy was performed. Pathologic examination of the lesion revealed a well-differentiated hepatoid carcinoma. The lesion was thought most likely to be a metastatic lesion from previous hepatocellular carcinoma. His alpha-fetoprotein level dropped to normal levels five months after the surgery.</p> <p>Conclusion</p> <p>This unusual intraluminal growing tumor proved to be a well-differentiated hepatoid carcinoma, most likely a metastatic lesion from previous hepatocellular carcinoma. This case reminds clinicians that in looking for likely hepatocellular carcinoma recurrence, when no detectable hepatic lesion can account for an elevated alpha-fetoprotein level, the gall bladder should be included in the search for the site of metastasis.</p
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